Update model config and README

README.md

@@ -2,7 +2,165 @@
 tags:
 - image-classification
 - timm
-- vision
-library_tag: timm
+library_name: timm
 license: apache-2.0
+datasets:
+- imagenet-1k
+- laion-2b
 ---
+# Model card for vit_base_patch16_clip_384.laion2b_ft_in1k
+
+A Vision Transformer (ViT) image classification model. Pretrained on LAION-2B image-text pairs using OpenCLIP. Fine-tuned on ImageNet-1k in `timm`. See recipes in [Reproducible scaling laws](https://arxiv.org/abs/2212.07143).
+
+
+## Model Details
+- **Model Type:** Image classification / feature backbone
+- **Model Stats:**
+  - Params (M): 86.9
+  - GMACs: 49.4
+  - Activations (M): 48.3
+  - Image size: 384 x 384
+- **Papers:**
+  - OpenCLIP: https://github.com/mlfoundations/open_clip
+  - Reproducible scaling laws for contrastive language-image learning: https://arxiv.org/abs/2212.07143
+  - LAION-5B: An open large-scale dataset for training next generation image-text models: https://arxiv.org/abs/2210.08402
+  - An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale: https://arxiv.org/abs/2010.11929v2
+- **Dataset:** ImageNet-1k
+- **Pretrain Dataset:**
+  - LAION-2B
+
+## Model Usage
+### Image Classification
+```python
+from urllib.request import urlopen
+from PIL import Image
+import timm
+
+img = Image.open(urlopen(
+    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
+))
+
+model = timm.create_model('vit_base_patch16_clip_384.laion2b_ft_in1k', pretrained=True)
+model = model.eval()
+
+# get model specific transforms (normalization, resize)
+data_config = timm.data.resolve_model_data_config(model)
+transforms = timm.data.create_transform(**data_config, is_training=False)
+
+output = model(transforms(img).unsqueeze(0))  # unsqueeze single image into batch of 1
+
+top5_probabilities, top5_class_indices = torch.topk(output.softmax(dim=1) * 100, k=5)
+```
+
+### Image Embeddings
+```python
+from urllib.request import urlopen
+from PIL import Image
+import timm
+
+img = Image.open(urlopen(
+    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
+))
+
+model = timm.create_model(
+    'vit_base_patch16_clip_384.laion2b_ft_in1k',
+    pretrained=True,
+    num_classes=0,  # remove classifier nn.Linear
+)
+model = model.eval()
+
+# get model specific transforms (normalization, resize)
+data_config = timm.data.resolve_model_data_config(model)
+transforms = timm.data.create_transform(**data_config, is_training=False)
+
+output = model(transforms(img).unsqueeze(0))  # output is (batch_size, num_features) shaped tensor
+
+# or equivalently (without needing to set num_classes=0)
+
+output = model.forward_features(transforms(img).unsqueeze(0))
+# output is unpooled, a (1, 577, 768) shaped tensor
+
+output = model.forward_head(output, pre_logits=True)
+# output is a (1, num_features) shaped tensor
+```
+
+## Model Comparison
+Explore the dataset and runtime metrics of this model in timm [model results](https://github.com/huggingface/pytorch-image-models/tree/main/results).
+
+## Citation
+```bibtex
+@software{ilharco_gabriel_2021_5143773,
+  author       = {Ilharco, Gabriel and
+                  Wortsman, Mitchell and
+                  Wightman, Ross and
+                  Gordon, Cade and
+                  Carlini, Nicholas and
+                  Taori, Rohan and
+                  Dave, Achal and
+                  Shankar, Vaishaal and
+                  Namkoong, Hongseok and
+                  Miller, John and
+                  Hajishirzi, Hannaneh and
+                  Farhadi, Ali and
+                  Schmidt, Ludwig},
+  title        = {OpenCLIP},
+  month        = jul,
+  year         = 2021,
+  note         = {If you use this software, please cite it as below.},
+  publisher    = {Zenodo},
+  version      = {0.1},
+  doi          = {10.5281/zenodo.5143773},
+  url          = {https://doi.org/10.5281/zenodo.5143773}
+}
+```
+```bibtex
+@article{cherti2022reproducible,
+  title={Reproducible scaling laws for contrastive language-image learning},
+  author={Cherti, Mehdi and Beaumont, Romain and Wightman, Ross and Wortsman, Mitchell and Ilharco, Gabriel and Gordon, Cade and Schuhmann, Christoph and Schmidt, Ludwig and Jitsev, Jenia},
+  journal={arXiv preprint arXiv:2212.07143},
+  year={2022}
+}
+```
+```bibtex
+@inproceedings{schuhmann2022laionb,
+  title={{LAION}-5B: An open large-scale dataset for training next generation image-text models},
+  author={Christoph Schuhmann and
+          Romain Beaumont and
+          Richard Vencu and
+          Cade W Gordon and
+          Ross Wightman and
+          Mehdi Cherti and
+          Theo Coombes and
+          Aarush Katta and
+          Clayton Mullis and
+          Mitchell Wortsman and
+          Patrick Schramowski and
+          Srivatsa R Kundurthy and
+          Katherine Crowson and
+          Ludwig Schmidt and
+          Robert Kaczmarczyk and
+          Jenia Jitsev},
+  booktitle={Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track},
+  year={2022},
+  url={https://openreview.net/forum?id=M3Y74vmsMcY}
+}
+```
+```bibtex
+@article{dosovitskiy2020vit,
+  title={An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale},
+  author={Dosovitskiy, Alexey and Beyer, Lucas and Kolesnikov, Alexander and Weissenborn, Dirk and Zhai, Xiaohua and Unterthiner, Thomas and  Dehghani, Mostafa and Minderer, Matthias and Heigold, Georg and Gelly, Sylvain and Uszkoreit, Jakob and Houlsby, Neil},
+  journal={ICLR},
+  year={2021}
+}
+```
+```bibtex
+@misc{rw2019timm,
+  author = {Ross Wightman},
+  title = {PyTorch Image Models},
+  year = {2019},
+  publisher = {GitHub},
+  journal = {GitHub repository},
+  doi = {10.5281/zenodo.4414861},
+  howpublished = {\url{https://github.com/huggingface/pytorch-image-models}}
+}
+```

model.safetensors

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